Support Vector Machine (SVM) [1]} is one of the conventional classifiers deployed in Speech Emotion Recognition Systems [2]}, [3]}, [4]}, [5]}, , , , , . The models built by an SVM classifier designate new training examples to any one category. Therefore, making this binary linear classifier non-probabilistic. Using a phenomenon known as kernel trick, SVMs can effectively execute a non-linear classification besides performing linear classification. As mentioned before, the language-independent vocal features [6]} such as fundamental frequency or pitch related features, intensity, etc., that are selected for SER have a better recognition rate when SVM is used [7]}, [8]}. Conventionally known as a separating hyperplane, a Support Vector Machine can be denoted as a discriminative classifier.
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